Novel GLP-1 Fusion Chimera as Potent Long Acting GLP-1 Receptor Agonist
Bibliographic record
Abstract
GLP-1 has a variety of anti-diabetic effects. However, native GLP-1 is not suitable for therapy of diabetes due to its short half-life (t1/2<2 min). To circumvent this, we developed a long-lasting GLP-1 receptor agonist by the fusion of GLP-1 with human IgG2 Fc (GLP-1/hIgG2). ELISA-based receptor binding assay demonstrated that GLP-1/hIgG2 had high binding affinity to the GLP-1R in INS-1 cells (Kd = 13.90±1.52 nM). Upon binding, GLP-1/hIgG2 was rapidly internalized by INS-1 cells in a dynamin-dependent manner. Insulin RIA showed that GLP-1/IgG2 dose-dependently stimulated insulin secretion from INS-1 cells. Pharmacokinetic studies in CD1 mice showed that with intraperitoneal injection (i.p.), the GLP-1/hIgG2 peaked at 30 minutes in circulation and maintained a plateau for >168 h. Intraperitoneal glucose tolerance test (IPGTT) in mice showed that GLP-1/hIgG2 significantly decreased glucose excursion. Furthermore, IPGTT performed on mice one week after a single drug-injection also displayed significantly reduced glucose excursion, indicating that GLP-1/hIgG2 fusion protein has long-lasting effects on the modulation of glucose homeostasis. GLP-1/hIgG2 was found to be effective in reducing the incidence of diabetes in multiple-low-dose streptozotocin-induced type 1 diabetes in mice. Together, the long-lasting bioactive GLP-1/hIgG2 retains native GLP-1 activities and thus may serve as a potent GLP-1 receptor agonist.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".